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RI:Small: Dynamic and Statistical Based Invariants on Manifolds for Video Analysis

RI:Small：用于视频分析的流形上基于动态和统计的不变量

基本信息

批准号：
1814631
负责人：
Octavia Camps
金额：
$ 50万
依托单位：
Northeastern University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1814631&HistoricalAwards=false
关键词：
RI Small Dynamic Statistical Based

项目摘要

Computer vision systems can benefit society in many ways. For example, spatially distributed vision sensors endowed with activity analysis capabilities can prevent crime, help optimize resource use in smart buildings, and give early warning of serious medical conditions. The most powerful computer vision systems employ an approach called "deep learning", in which simulated networks of neurons transform the input video pixels into high-level concepts. For example, in the crime example, the high-level concept might be "someone breaking into a building". A major impediment to building computer vision is that great expertise and trial-and-error is required for a programmer to design a neural network that can teach itself to recognize the goal concepts. This project will reduce this barrier by creating a set of well-designed neural network modules, or "layers", that a programmer can snap together to build a working computer vision system. Education is proactively integrated into this project, starting with STEM summer camps projects for urban middle school students and continuing at the college level with a multi-disciplinary program that uses the grand challenge of aware environments to link a full range of distinct subjects ranging from computer vision and machine learning to systems theory and optimization. At the graduate level, these activities are complemented by recruitment efforts that leverage the resources at Northeastern's University Program in Multicultural Engineering to broaden the participation of underrepresented groups in research. Computer vision has made tremendous progress in the era of deep learning. However, training of deep architectures requires learning the optimal value of a very large number of parameters through the numerical minimization of a non-convex loss function. While in practice, using stochastic gradient descent to solve this problem often "works", the analysis of what the network learned or why it failed to do so, remains an a-posteriori task requiring visualization tools to inspect which neurons are firing and possibly to look at intermediate results. This research seeks to address this issue by incorporating a set of structured layers to current deep architectures, designed using dynamical systems theory and statistics fundamentals, which capture spatio-temporal information across multiple scales. At its core is a unified vision, invariants on latent space manifolds as information encapsulators, that emphasizes robustness and computational complexity issues. Advantages of the proposed layers include the ability to easily understand what they learn, since they are based on first principles; shallower networks with a reduction of the number of parameters that needs to be learned due to the high expressive power of the new layers; and requiring less annotated data, by providing efficient ways to transfer knowledge between domains and to synthesize realistic data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

计算机视觉系统可以在许多方面造福社会。例如，具有活动分析功能的空间分布式视觉传感器可以预防犯罪，帮助优化智能建筑中的资源使用，并对严重的医疗状况发出预警。最强大的计算机视觉系统采用一种称为“深度学习”的方法，其中模拟的神经元网络将输入的视频像素转换为高级概念。例如，在犯罪示例中，高级概念可能是“有人闯入建筑物”。构建计算机视觉的一个主要障碍是，程序员需要大量的专业知识和试错来设计一个可以自我学习识别目标概念的神经网络。该项目将通过创建一组精心设计的神经网络模块或“层”来减少这一障碍，程序员可以将这些模块组合在一起构建一个工作的计算机视觉系统。教育被积极地融入到这个项目中，从城市中学生的STEM夏令营项目开始，并在大学阶段继续进行多学科项目，该项目利用感知环境的巨大挑战将从计算机视觉和机器学习到系统理论和优化的各种不同学科联系起来。在研究生阶段，这些活动得到了招聘工作的补充，这些招聘工作利用了东北大学多元文化工程项目的资源，以扩大代表性不足的群体在研究中的参与。计算机视觉在深度学习时代取得了巨大的进步。然而，深度架构的训练需要通过非凸损失函数的数值最小化来学习大量参数的最优值。虽然在实践中，使用随机梯度下降来解决这个问题通常是“有效的”，但分析网络学到了什么或为什么它没有这样做，仍然是一项后验任务，需要可视化工具来检查哪些神经元正在放电，并可能查看中间结果。本研究旨在通过将一组结构化层结合到当前的深度架构中来解决这个问题，该架构使用动态系统理论和统计学基础设计，可以跨多个尺度捕获时空信息。其核心是一个统一的愿景，潜在空间流形上的不变量作为信息分解器，强调鲁棒性和计算复杂性问题。所提出的层的优点包括能够容易地理解它们所学习的内容，因为它们是基于第一原理的;由于新层的高表达能力，需要学习的参数数量减少的较浅的网络;并且需要较少的注释数据，该奖项反映了NSF的法定使命，通过使用基金会的知识价值和更广泛的影响审查标准进行评估，认为值得支持。